Predictive Analytics Cheat Sheet
|
|
- Hortense Cooper
- 6 years ago
- Views:
Transcription
1 Predictive Analytics The use of advanced technology to help legal teams separate datasets by relevancy or issue in order to prioritize documents for expedited review. Often referred to as Technology Assisted Review (TAR) or Predictive Coding (PC). ALGORITHM A specified series of computations executed to accomplish a goal. AMBIGUOUS DOCUMENTS Documents for which the system cannot achieve a sufficiently clear relevance determination. These documents must be reviewed by attorneys. BULK CODING / BULK TAGGING The process of coding all members of a group of documents based on the review of only a few members of the group. CONCEPT-BASED PREDICTIVE ANALYTICS System analyzes the meaning and context of words used within a set of documents and translates that information into mathematical models. Once a model has been built, a find more like these algorithm is applied to the document population to identify documents that are similar in conceptual content. Concept-based Predictive Analytics is most effective when trying to find documents that closely resemble each other. CATEGORIZATION The process by which documents are grouped into specific categories in order to identify relationships. Categorization is performed with human interaction. CLUSTERING also known as THEMES System automatically organizes a document population into smaller subset groups based on conceptual content. These groupings are created and organized purely by the algorithm s classification without human interaction so clustering is most effective when the reviewer has little knowledge of the data content. CONCEPT SEARCH Using an internal language model, an analytics system uncovers and identifies document relationships within and across datasets. Concept search is used to find documents beyond those that would be returned by a simple keyword search and/ or Boolean search.
2 NEAR-DUPLICATE DETECTION The process of comparing electronic documents within a document population based on text content (not metadata) and then using that information to identify similar or duplicate versions of those documents across additional datasets. THREADING Analyzes a set of s based on text content and then groups s from the same conversation string. By identifying the most inclusive as a single point of review, prior versions can be set aside. CONFIDENCE LEVEL A measure indicating the overall reliability of sample-based estimates. It is the probability that a population parameter will fall between two set values. This measure can take any number of probabilities, with the most common being 95% or 99%. For example, 95% confidence means that if one were to draw 100 independent random samples of the same size, and compute the confidence interval from each sample, about 95 of the 100 confidence intervals would contain the true value. CONFUSION MATRIX A table that allows visualization and evaluation of the performance of the algorithm(s). FALSE NEGATIVE A relevant document that is incorrectly identified as non-relevant. FALSE POSITIVE A non-relevant document that is incorrectly identified as relevant. TRUE NEGATIVE A non-relevant document that is correctly identified as such. TRUE POSITIVE A relevant document that is correctly identified as such. DATASET A collection of documents specific to a case/matter.
3 F-MEASURE A balance between recall and precision. A higher F-Measure typically indicates higher precision and recall, while a lower F-Measure suggests lower precision and recall. Currently, there is no industry standard for an appropriate F-Measure, and it is up to the parties involved to define, depending on the particular needs of the case. MACHINE LEARNING The use of computer algorithms to organize or classify documents by analyzing their content and features. ACTIVE LEARNING System strategically chooses a document (often based on uniqueness) for which a reviewer makes a relevance decision. The system learns from these determinations and chooses the next set of exemplars to maximize its learning (ex. predictive coding). SUPERVISED LEARNING System uses subject matter experts coding decisions on a training set of documents in order to tag and rank the remaining documents in the collection based on similarity to the training dataset (ex. categorization). UNSUPERVISED LEARNING Documents are automatically organized, grouped and labeled by the system without any human interaction (ex. clustering). NON-TEXT DOCUMENTS Files (such as photos, poor-quality scans or electronic documents with security restrictions) that are not able to be considered by Predictive Analytics systems because the advanced technology is based solely on the text content of documents. POTENTIALLY PRIVILEGED DOCUMENTS These documents must be reviewed by legal teams in order to confirm that the content is indeed privileged. PRECISION A measure of exactness (actual relevant documents retrieved/total number of documents retrieved); what percent of a given dataset is relevant?
4 PREDICTIVE CODING A Predictive Analytics process involving the use of an active learning algorithm to distinguish relevant from non-relevant documents, based on subject matter experts coding decisions on a training set of documents. QUALITY CONTROL Methods to validate and ensure that reasonable results are being achieved during a review effort, especially when advanced technology is being utilized. CHECKLIST A record of the tasks performed which helps to mitigate the risk of error. DOCUMENT SEEDING Presenting the system with documents subject matter experts have already deemed relevant in order to better train algorithms within Predictive Analytics systems. OVERTURN CORRECTION A workflow utilized by legal teams to reverse a Predictive Analytics system s incorrect document classifications. TRACKING Linking documents back to the source media on which they were collected as well as to specific workflows. This produces a traceable record of data collections, processing, review and productions in order to provide chain of custody documentation. RECALL A measure of completeness (actual relevant documents retrieved/total actual relevant documents); what percent of the relevant documents were retrieved by the algorithm? RELEVANT DOCUMENT A document with content that pertains to the subject matter outlined in a production request. Not all relevant documents are responsive, but all responsive documents are relevant. RELEVANCE Denotes how closely a document pertains to the matter at hand.
5 RESPONSIVE DOCUMENT A document that actually meets the information needs of a party s production request. All responsive documents are relevant, but not all relevant documents are responsive. RESPONSIVENESS Denotes how well a document meets the information need of an opposing party. SAMPLING as used in ediscovery The process of selecting a representative part of a dataset for the purpose of identifying search terms and determining relevance. STABILIZATION Occurs when a Predictive Analytics system has learned all it can in order to predict relevance. SUBJECT MATTER EXPERT (SME) An individual who is familiar with the case information and issues and can make a determination as to the relevance of a particular document. SUPPORT VECTOR-BASED PREDICTIVE ANALYTICS also known as PREDICTIVE CODING Supervised learning models with an algorithm that analyzes data to recognize content patterns and are used for classifying by regression analysis. TRAINING DOCUMENTS Documents that help Predictive Analytics applications learn how legal teams would handle a specific document. They are distinguished between relevant and non-relevant in order to give the technology the required inputs to make future classifications. VALIDATION SAMPLES Confirm the performance of Predictive Analytics algorithms.
6 About D4 D4 is a national provider of electronic discovery, computer forensics, information security and management, and deposition services to law firms and corporations, and has been instrumental in helping customers realize up to a 70% cost reduction over previous ediscovery solutions. At D4, we focus on technology and process to streamline the discovery lifecycle in the most defensible, practical and cost-effective manner possible. We believe that ediscovery doesn t have to break the bank and we make that belief a reality for clients every day. Founded in 1997 in Upstate New York, D4 has grown to a national presence. With over 160 employees, D4 has offices in Buffalo, Chicago, Detroit, Grand Rapids, Lincoln, New York City, Omaha, Orlando, Phoenix, Rochester, San Francisco, San Diego and Tampa. D4 s state-of-theart Tier 3 data center and operations in Rochester are complemented by electronic discovery, litigation support and paper document services in other offices across the country. D4 has been recognized by Inc. Magazine as one of the fastest-growing private companies in the US, and is a four-time Inc. 500/5000 honoree. There s a reason why hundreds of AMLAW 200 firms and Fortune 1000 companies choose D4. Our unprecedented customer service, coupled with our industry experts and best-of-breed technology, is why the D4 way is the better way. marketing@d4discovery.com
Predictive Coding Defensibility: The Symantec Transparent Predictive Coding Workflow
WHITE PAPER: PREDICTIVE CODING DEFENSIBILITY........................................ Predictive Coding Defensibility: The Symantec Transparent Predictive Coding Workflow Who should read this paper Predictive
More informationEdiscovery White Paper US. The Ultimate Predictive Coding Handbook. A comprehensive guide to predictive coding fundamentals and methods.
Ediscovery White Paper US The Ultimate Predictive Coding Handbook A comprehensive guide to predictive coding fundamentals and methods. 2 The Ultimate Predictive Coding Handbook by KLDiscovery Copyright
More informationImprove FOIA and congressional request responses Reduce time and cost, increase efficiency, and maintain consistency: Leveraging Discovery Techniques
Reduce time and cost, increase efficiency, and maintain consistency: Leveraging Discovery Techniques to Improve Traditional and Congressional Request Responses Introduction Traditionally, government agencies
More informationGoodbye Starts & Stops... Hello. Goodbye Data Batches... Goodbye Complicated Workflow... Introducing
Goodbye Starts & Stops... Hello Goodbye Data Batches... Goodbye Complicated Workflow... Introducing Introducing Automated Digital Discovery (ADD ) The Fastest Way to Get Data Into Review Automated Digital
More informationHow to Achieve Discovery Workflow Nirvana
How to Achieve Discovery Workflow Nirvana A New Approach to Efficient, Cost-Effective, and Repeatable ediscovery The Costs of Inefficient ediscovery Workflows Review is the most expensive piece of the
More informationUsing Metrics Through the Technology Assisted Review (TAR) Process. A White Paper
Using Metrics Through the Technology Assisted Review (TAR) Process A White Paper 1 2 Table of Contents Introduction....4 Understanding the Data Set....4 Determine the Rate of Relevance....5 Create Training
More informationThe Art of ediscovery
WHITE PAPER The Art of ediscovery Not All Providers are Created Equal ediscovery in the Information Age It s no secret that the expanding volume of electronically stored information (ESI) involved in a
More informationDocument and Media Exploitation (DOMEX)
SOLUTION BRIEF: DOCUMENT AND MEDIA EXPLOITATION (DOMEX)........................................ Document and Media Exploitation (DOMEX) Who should read this paper DOMEX analysts looking to quickly prioritize,
More informationediscovery
ediscovery Evolving methods of communication, advances in technology, and the myriad ways information is stored have drastically changed how businesses operate and litigate. Courts have ruled that most
More informationTECHNOLOGY BRIEF Intapp and Applied AI. THE INTAPP PROFESSIONAL SERVICES PLATFORM Unified Client Life Cycle
TECHNOLOGY BRIEF Intapp and Applied AI Technology assisted decision-making for professional services firms Professional services firms are struggling to extract valuable insights from their data, hindered
More informationSymantec ediscovery Platform, powered by Clearwell
Symantec ediscovery Platform, powered by Clearwell Data Sheet: Archiving and ediscovery The brings transparency and control to the electronic discovery process. From collection to production, our workflow
More informationING3NIOUS NORTHEAST EDISCOVERY & IG RETREAT
MEASURE TWICE, DISCOVER ONCE ING3NIOUS NORTHEAST EDISCOVERY & IG RETREAT Stephen Whetstone Thomas Barnett Adam Beschloss Michael Sugrue James Zucker Stroz Friedberg Paul Hastings QuisLex, Inc. Stroz Friedberg
More informationebook TOP FIVE TIPS AND TRICKS FOR REDUCING EDISCLOSURE COSTS PAGE 1 library
ebook library PAGE 1 TOP FIVE TIPS AND TRICKS FOR REDUCING EDISCLOSURE COSTS TOP FIVE TIPS AND TRICKS FOR REDUCING EDISCLOSURE COSTS The abundance of electronic information created and stored by large
More informationWeek 1 Unit 1: Intelligent Applications Powered by Machine Learning
Week 1 Unit 1: Intelligent Applications Powered by Machine Learning Intelligent Applications Powered by Machine Learning Objectives Realize recent advances in machine learning Understand the impact on
More informationDSi Pilot Program: Comparing Catalyst Insight Predict with Linear Review
case study DSi Pilot Program: Comparing Catalyst Insight Predict with Linear Review www.dsicovery.com 877-797-4771 414 Union St., Suite 1210 Nashville, TN 37219 (615) 255-5343 Catalyst Insight Predict
More informationUnlocking the Power of Big Data Analytics for Application Security and Security Operation
Unlocking the Power of Big Data Analytics for Application Security and Security Operation Virginia Lee, Senior Security Architect CISSP, CISA, CEH 1 September 2018 What is Machine Learning? Learning from
More informationVeritas TM ediscovery Platform
Veritas TM ediscovery Platform VERITAS ediscovery PLATFORM The Veritas TM ediscovery Platform is the leading enterprise ediscovery solution that enables enterprises, governments, and law firms to manage
More informationBUSINESS DEVELOPMENT INTELLIGENCE MONITORING FOR LAW FIRMS
WHITEPAPER BUSINESS DEVELOPMENT INTELLIGENCE MONITORING FOR LAW FIRMS Most major law firms engage in intelligence monitoring to spot new business opportunities and inform attorneys about important developments
More informationPut critical information to work. Proven resources and expertise help you deliver for your clients. Pitney Bowes Legal Solutions
Put critical information to work. Proven resources and expertise help you deliver for your clients Pitney Bowes Legal Solutions You deliver for your clients. Let us deliver for you. PITNEY BOWES DELIVERS
More informationHow Artificial Intelligence can help during the Due Diligence process for Mergers & Acquisitions. Complimentary ebook offered by
How Artificial Intelligence can help during the Due Diligence process for Mergers & Acquisitions Complimentary ebook offered by THE CHALLENGE WITH EVERY M&A PROCESS: FINDING RELEVANT INFORMATION In all
More informationCan Predictive Coding Add Value to Your Case? A Quick Assessment Tool
WHITE PAPER Can Predictive Coding Add Value to Your Case? A Quick Assessment Tool Developed by Sophie Ross, Senior Managing Director John Murdock, Senior Director, Technology-Assisted Review Predictive
More informationSpeech Analytics Transcription Accuracy
Speech Analytics Transcription Accuracy Understanding Verint s speech analytics transcription and categorization accuracy Verint.com Twitter.com/verint Facebook.com/verint Blog.verint.com Table of Contents
More informationUSING R IN SAS ENTERPRISE MINER EDMONTON USER GROUP
USING R IN SAS ENTERPRISE MINER EDMONTON USER GROUP INTRODUCTION PAT VALENTE, MA Solution Specialist, Data Sciences at SAS. Training in Economics and Statistics. 20 years experience in business areas including
More informationModel Selection, Evaluation, Diagnosis
Model Selection, Evaluation, Diagnosis INFO-4604, Applied Machine Learning University of Colorado Boulder October 31 November 2, 2017 Prof. Michael Paul Today How do you estimate how well your classifier
More informationGOVERNMENT ANALYTICS LEADERSHIP FORUM SAS Canada & The Institute of Public Administration of Canada. April 26 The Shaw Centre
GOVERNMENT ANALYTICS LEADERSHIP FORUM SAS Canada & The Institute of Public Administration of Canada April 26 The Shaw Centre Ottawa Artificial Intelligence Algorithms Automation Deep Learning Machine Learning
More informationIdentifying Splice Sites Of Messenger RNA Using Support Vector Machines
Identifying Splice Sites Of Messenger RNA Using Support Vector Machines Paige Diamond, Zachary Elkins, Kayla Huff, Lauren Naylor, Sarah Schoeberle, Shannon White, Timothy Urness, Matthew Zwier Drake University
More informationAdministration of Clearwell ediscovery Platform 7.x Study Guide
Administration of Clearwell ediscovery Platform 7.x Study Guide The following tables list the Symantec SCS Certification exam objectives for the Administration of Clearwell ediscovery Platform 7.x exam
More informationSegmentation Modeling
Deepen Customer Understanding There is an infinitely wide gamut of human behavior, interests and characteristics to account for as a modern business operating in the online marketplace. To understand consumers,
More informationLitigation Support & Legal Document Services
Litigation Support & Legal Document Services Offering specialised products and services, coupled with proven techniques, Altlaw provides technological expertise in electronic litigation support and legal
More informationDeveloping A Cost-Effective, Reproducible, Defensible E- Discovery Paradigm Using Equivio>Relevance. White Paper
Developing A Cost-Effective, Reproducible, Defensible E- Discovery Paradigm Using Equivio>Relevance White Paper INTRODUCTION In civil litigation, fact discovery, especially the process of reviewing documents
More informationWARRANTY WASTE ANALYTICS
WARRANTY WASTE ANALYTICS February 2017 WARRANTY WASTE ANALYTICS SOLUTION Problems and opportunities driving a warranty claims fraud management solution Challenges Opportunities Warranty Claims Fraud Analytics
More informationAdvantage. Transforming Big Data Into The Big Picture. Optimized Workflow
The Advantage Transforming Big Data Into The Big Picture Interactive Visual Analysis Visualize date ranges, social networks, communication patterns, concepts and keywords to quickly identify notable themes
More informationGlobal Information Governance: Cross-border Records Management the Hard [copy] Way
Global Information Governance: Cross-border Records Management the Hard [copy] Way A case study on new technology applied to persistent global records management challenges Hard copy records continue to
More informationAutomatic Tagging and Categorisation: Improving knowledge management and retrieval
Automatic Tagging and Categorisation: Improving knowledge management and retrieval 1. Introduction Unlike past business practices, the modern enterprise is increasingly reliant on the efficient processing
More informationWhitepaper: Enterprise Vault Discovery Accelerator and Clearwell A Comparison August 2012
888.427.5505 Whitepaper: Enterprise Vault Discovery Accelerator and Clearwell A Comparison August 2012 Prepared by Dan Levine, Principal Engineer & Miguel Ortiz, Esq., ediscovery Specialist Globanet 16501
More informationOnline appendix for THE RESPONSE OF CONSUMER SPENDING TO CHANGES IN GASOLINE PRICES *
Online appendix for THE RESPONSE OF CONSUMER SPENDING TO CHANGES IN GASOLINE PRICES * Michael Gelman a, Yuriy Gorodnichenko b,c, Shachar Kariv b, Dmitri Koustas b, Matthew D. Shapiro c,d, Dan Silverman
More informationEnhancing Risk and Internal Audit effectiveness using KNIME. KNIME Fall Summit November 2017
Enhancing Risk and Internal Audit effectiveness using KNIME KNIME Fall Summit November 2017 Background Project Problem Solution Outcome 2 1 st Line of Defence Leadership Functions Reports to Executive
More informationLeveraging the Cloud for a Force Multiplier v. Goliath
A Field Guide for the Boutique Firm: Utilizing On-Demand Technology and Workflows to Gain a Competitive Advantage with Limited Resources About our Webinars Webinars take place monthly and cover a variety
More informationCopyr i g ht 2012, SAS Ins titut e Inc. All rights res er ve d. ENTERPRISE MINER: ANALYTICAL MODEL DEVELOPMENT
ENTERPRISE MINER: ANALYTICAL MODEL DEVELOPMENT ANALYTICAL MODEL DEVELOPMENT AGENDA Enterprise Miner: Analytical Model Development The session looks at: - Supervised and Unsupervised Modelling - Classification
More informationHow the Ethics Rules Influence the Role of Discovery Counsel
How the Ethics Rules Influence the Role of Discovery Counsel This article was originally published by Corporate Counsel on November 17, 2015. by David L. Stanton and Brian D. Martin David L. Stanton Litigation
More informationDeloitte Forensic Predict. Detect. Respond.
Deloitte Forensic Predict. Detect. Respond. 2 Predict. Detect. Respond. Deloitte Forensic Predict. Detect. Respond. About us The Deloitte Forensic team helps clients navigate and resolve crisis, controversy,
More informationHOW TO APPLY YOUR TAXONOMY TO YOUR CONTENT: USING AUTOCATEGORIZATION TO INDEX
HOW TO APPLY YOUR TAXONOMY TO YOUR CONTENT: USING AUTOCATEGORIZATION TO INDEX SLA 2013 Annual Conference & INFO-EXPO, June 10, 2013 Paula McCoy Managing Editor, Science & Taxonomy ProQuest Editorial Operations
More informationFive Questions to Ask Your TAR Vendor About Continuous Active Learning
877.557.4273 catalystsecure.com ARTICLE Five Questions to Ask Your TAR Vendor About Continuous Active Learning How to Ensure that Your Review Platform Uses the Most Effective Technology Thomas Gricks,
More informationSPM 8.2. Salford Predictive Modeler
SPM 8.2 Salford Predictive Modeler SPM 8.2 The SPM Salford Predictive Modeler software suite is a highly accurate and ultra-fast platform for developing predictive, descriptive, and analytical models from
More informationDow Jones Industry, Region and Subject Taxonomy
Dow Jones Industry, Region and Subject Taxonomy Features, Benefits and Application Dow Jones provides this taxonomy of industry, region, subject and company/organisation codes under the name Dow Jones
More informationWebscraping job vacancies ESSNet face to face meeting. Country presentation #2: France
Webscraping job vacancies ESSNet face to face meeting Country presentation #2: France Outline 1. Data Access 2. Data Handling 3. Methodology 4. Statistical Outputs ESSNet meeting 2 Scraped data Experimental
More informationDiscover A Better Way
Discover A Better Way Times have changed. In just a few short years, electronic discovery has evolved from a tool employed in only the largest, most document-intensive cases into a mainstream practice
More informationProfessor Dr. Gholamreza Nakhaeizadeh. Professor Dr. Gholamreza Nakhaeizadeh
Statistic Methods in in Mining Business Understanding Understanding Preparation Deployment Modelling Evaluation Mining Process (( Part 3) 3) Professor Dr. Gholamreza Nakhaeizadeh Professor Dr. Gholamreza
More informationTABLE OF CONTENTS ix
TABLE OF CONTENTS ix TABLE OF CONTENTS Page Certification Declaration Acknowledgement Research Publications Table of Contents Abbreviations List of Figures List of Tables List of Keywords Abstract i ii
More information2017 COMPENSATION TRENDS IN LEGAL TECHNOLOGY
2017 COMPENSATION TRENDS IN LEGAL TECHNOLOGY Survey Results Report The Cowen Group 747 Third Avenue, New York (212) 661 0025 Contact kayla@cowengroup.com 2 SURVEY RESPONDENT DEMOGRAPHICS Respondents Demographics
More informationComponents of a semantic enterprise
Components of a semantic enterprise Gary Carlson, Gary Carlson Consulting Christine Connors, TriviumRLG LLC Semantic Technology Conference San Francisco, CA June 23, 2010 Gary Carlson Gary Carlson brings
More informationINTELLIGENCE COMMUNITY DIRECTIVE NUMBER 208
INTELLIGENCE COMMUNITY DIRECTIVE NUMBER 208 WRITE FOR MAXIMUM UTILITY (EFFECTIVE : 17 DECEMBER 2008) A. AUTHORITY : The National Security Act of 1947, as amended ; Executive Order (EO) 12958, as amended
More informationQuality Assessment Method for Software Development Process Document based on Software Document Characteristics Metric
Quality Assessment Method for Software Development Process Document based on Software Document Characteristics Metric Patra Thitisathienkul, Nakornthip Prompoon Department of Computer Engineering Chulalongkorn
More informationA logistic regression model for Semantic Web service matchmaking
. BRIEF REPORT. SCIENCE CHINA Information Sciences July 2012 Vol. 55 No. 7: 1715 1720 doi: 10.1007/s11432-012-4591-x A logistic regression model for Semantic Web service matchmaking WEI DengPing 1*, WANG
More informationVDMML Enablement Session Data Science Jam Sessions
VDMML Enablement Session Data Science Jam Sessions May 3 rd, 2018 Joline Jammaers, Pre-Sales Analytical Consultant Véronique Van Vlasselaer, Pre-Sales Analytical Consultant Your presenters Véronique Van
More informationLeveraging Data Analytics for Customer Support Efficiency
Leveraging Data Analytics for Customer Support Efficiency October 6, 2016 Kelly Hoopes Senior Consultant Service Strategies Overview Discussion Topics Types of Data Structured vs. Unstructured Reporting
More informationORACLE ADVANCED FINANCIAL CONTROLS CLOUD SERVICE
ORACLE ADVANCED FINANCIAL CONTROLS CLOUD SERVICE Advanced Financial Controls (AFC) Cloud Service enables continuous monitoring of all expense and payables transactions in Oracle ERP Cloud, for potential
More informationAn Implementation of genetic algorithm based feature selection approach over medical datasets
An Implementation of genetic algorithm based feature selection approach over medical s Dr. A. Shaik Abdul Khadir #1, K. Mohamed Amanullah #2 #1 Research Department of Computer Science, KhadirMohideen College,
More informationArcadia Operating: Intelligent Well File Streamlines Acquisition Process
Arcadia Operating: Intelligent Well File Streamlines Acquisition Process Arcadia Operating has a successful track record of growth by participating in the acquisition of operated oil & gas properties across
More informationLegal Services Use Cases
Legal Services Use Cases Legal Services Priorities Legal Services firms are heavy with agreements and notices that require extensive preparation, signing, tracking and archiving. Law firms, in particular,
More informationHOW TO USE OFFICE 365 AND X1 DISCOVERY TO ACHIEVE YOUR EDISCOVERY GOALS
HOW TO USE OFFICE 365 AND X1 DISCOVERY TO ACHIEVE YOUR EDISCOVERY GOALS AGENDA Office 365 ediscovery and Compliance features and functions X1 Distributed Discovery features and demo Q&A/Open Discussion
More informationConcept Searching is unique. No other statistical search and classification vendor puts compound terms in their index. This technique delivers high
1 2 Concept Searching is unique. No other statistical search and classification vendor puts compound terms in their index. This technique delivers high precision without the loss of recall. Why Classification?
More informationThe leading parts and logistics management system.
INTERACTIVE VERSION Haystack Gold The leading parts and logistics management system. Powering new levels of savings, efficiency and risk mitigation in design, supply chain and obsolescence management.
More informationBuilding A Defensible Search And Review Process For ESI
Building A Defensible Search And Review Process For ESI Deborah H. Juhnke It s time to go beyond the keyword search. Deborah H. Juhnke is an e-discovery consultant at Husch Blackwell Sanders LLP, in Kansas
More informationCognitive Data Governance
IBM Unified Governance & Integration White Paper Powered by Machine Learning to find and use governed data Jo Ramos Distinguished Engineer & Director IBM Analytics Rakesh Ranjan Program Director & Data
More informationBuilding the In-Demand Skills for Analytics and Data Science Course Outline
Day 1 Module 1 - Predictive Analytics Concepts What and Why of Predictive Analytics o Predictive Analytics Defined o Business Value of Predictive Analytics The Foundation for Predictive Analytics o Statistical
More informationModels in Engineering Glossary
Models in Engineering Glossary Anchoring bias is the tendency to use an initial piece of information to make subsequent judgments. Once an anchor is set, there is a bias toward interpreting other information
More informationKnowledgeSTUDIO. Advanced Modeling for Better Decisions. Data Preparation, Data Profiling and Exploration
KnowledgeSTUDIO Advanced Modeling for Better Decisions Companies that compete with analytics are looking for advanced analytical technologies that accelerate decision making and identify opportunities
More informationInsourcing and Outsourcing Discovery Tasks: Lessons in Adding Value & Minimizing Risk
Insourcing and Outsourcing Discovery Tasks: Lessons in Adding Value & Minimizing Risk Checklist for Decision-Making Understanding Your Organization and the Market 1. Outsourcing process and technology:
More informationBig Data. Methodological issues in using Big Data for Official Statistics
Giulio Barcaroli Istat (barcarol@istat.it) Big Data Effective Processing and Analysis of Very Large and Unstructured data for Official Statistics. Methodological issues in using Big Data for Official Statistics
More informationPREDICTING EMPLOYEE ATTRITION THROUGH DATA MINING
PREDICTING EMPLOYEE ATTRITION THROUGH DATA MINING Abbas Heiat, College of Business, Montana State University, Billings, MT 59102, aheiat@msubillings.edu ABSTRACT The purpose of this study is to investigate
More informationCapture & Proposal Process Consulting
Capture & Proposal Process Consulting Did you know that having the right capture and proposal process can raise your win probability, increase your new business revenue, and decrease your cost of new business
More informationPredicting and Explaining Price-Spikes in Real-Time Electricity Markets
Predicting and Explaining Price-Spikes in Real-Time Electricity Markets Christian Brown #1, Gregory Von Wald #2 # Energy Resources Engineering Department, Stanford University 367 Panama St, Stanford, CA
More informationInferring Gene-Gene Interactions and Functional Modules Beyond Standard Models
Inferring Gene-Gene Interactions and Functional Modules Beyond Standard Models Haiyan Huang Department of Statistics, UC Berkeley Feb 7, 2018 Background Background High dimensionality (p >> n) often results
More informationPAST research has shown that real-time Twitter data can
Algorithmic Trading of Cryptocurrency Based on Twitter Sentiment Analysis Stuart Colianni, Stephanie Rosales, and Michael Signorotti ABSTRACT PAST research has shown that real-time Twitter data can be
More informationARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
August 201 I n f l u e n ce and insight t h ro u g h social media The Success of ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Requires an Architectural Approach to Infrastructure W H I T E P A P E R Prepared
More informationAn Arbitrator s Guide to Successfully Resolving ediscovery Disputes. By: Alison A. Grounds and Kenneth C. Gibbs
An Arbitrator s Guide to Successfully Resolving ediscovery Disputes By: Alison A. Grounds and Kenneth C. Gibbs I. Introduction Disputes related to the preservation, collection, review, production and use
More informationUnderstanding the Voice of the Customer
Business white paper Understanding the Voice of the Customer How to effectively leverage customer insight with business discovery analytics Table of contents 3 Today s diverse brand interactions 3 A new
More informationECLT 5810 E-Commerce Data Mining Techniques - Introduction. Prof. Wai Lam
ECLT 5810 E-Commerce Data Mining Techniques - Introduction Prof. Wai Lam Data Opportunities Business infrastructure have improved the ability to collect data Virtually every aspect of business is now open
More informationAccelerate GDPR compliance with the Microsoft Cloud Samuel Marín Sr. Sales Solutions Specialist
Accelerate GDPR compliance with the Microsoft Cloud Samuel Marín Sr. Sales Solutions Specialist This presentation is intended to provide an overview of GDPR and is not a definitive statement of the law.
More informationHow Business Analysis Can Improve Sales and Marketing Outcomes
How Business Analysis Can Improve Sales and Marketing Outcomes In today s environment, the strategic focus for most organizations is revenue growth. Almost all executives are searching for ways to drive
More informationThe Data Difference in
The Data Difference in Next Generation 9-1-1 Systems A white paper from FE/Kimball May 2010 Page 2 Key terminology: GIS - geographic information system ALI - automatic location identification MSAG - master
More informationDeloitte Discovery Advisory Enabling an agile response to discovery, investigatory, and regulatory requests
Deloitte Discovery Advisory Enabling an agile response to discovery, investigatory, and regulatory requests 2018 Deloitte Discovery Advisory The Deloitte approach Deloitte Discovery Advisory The Deloitte
More informationDavid Wu, ARMA Spring Workshops 2015, Ottawa, April 28,
Taxonomies Improving Business Decisions and Taxonomy Basics Understand ECM/RM Differences with SharePoint 2010 and 2013 How to architect/configure SharePoint 2013 and Content Server AGA David Wu ARMA Spring
More informationEarly Information Assessment: The first 72 hours of an investigation
Early Information Assessment: The first 72 hours of an investigation Daniel Torpey daniel.torpey@ey.com Dave Rogers dave.rogers@ey.com Learning objectives Increase awareness of ediscovery issues Effective
More informationFinancial Services Compliance
Financial Services Compliance Amidst frequently changing regulations, heightened risk, and a growing volume of disparate data, compliance has become complex, inefficient, and costly. Mitigating new risk
More informationThe Business Value of Taxonomy
The Business Value of Taxonomy August 1, 2012 Gary Kahn Earley & Associates Overview Founded - 1994 Headquarters - Boston, MA What we do Design and deliver content management and search solutions for companies
More informationText Analytics for Executives Title
WHITE PAPER Text Analytics for Executives Title What Can Text Analytics Do for Your Organization? ii Contents Introduction... 1 Getting Started with Text Analytics... 2 Text Analytics in Government...
More informationMachine Learning Techniques For Particle Identification
Machine Learning Techniques For Particle Identification 06.March.2018 I Waleed Esmail, Tobias Stockmanns, Michael Kunkel, James Ritman Institut für Kernphysik (IKP), Forschungszentrum Jülich Outlines:
More informationTT SCORE. Trade Surveillance with Machine Learning THE NEED FOR TRADE SURVEILLANCE TRADITIONAL PARAMETER-BASED SURVEILLANCE TOOLS
TT SCORE Trade Surveillance with Machine Learning THE NEED FOR TRADE SURVEILLANCE The financial industry has experienced great change and extraordinary challenges in the wake of the 2008 global financial
More informationData Mining in CRM THE CRM STRATEGY
CHAPTER ONE Data Mining in CRM THE CRM STRATEGY Customers are the most important asset of an organization. There cannot be any business prospects without satisfied customers who remain loyal and develop
More informationAnalysis of a Proposed Universal Fingerprint Microarray
Analysis of a Proposed Universal Fingerprint Microarray Michael Doran, Raffaella Settimi, Daniela Raicu, Jacob Furst School of CTI, DePaul University, Chicago, IL Mathew Schipma, Darrell Chandler Bio-detection
More informationCHAPTER 2 LITERATURE SURVEY
10 CHAPTER 2 LITERATURE SURVEY This chapter provides the related work that has been done about the software performance requirements which includes the sub sections like requirements engineering, functional
More informationData Preparation and the Question of Data Quality. Harald Smith, Director, Product Management
Data Preparation and the Question of Data Quality Harald Smith, Director, Product Management Speaker Harald Smith Director of Product Management Trillium Software ~20 years in Information Management incl.
More informationManaging Sensitive Records Using SharePoint Server
Managing Sensitive Records Using SharePoint Server Contents Executive Summary Current State Analysis Future State Analysis Business Case Adoption & Implementation Planning Next Steps 2 Executive Summary
More informationTaking Control of Open Source Software in Your Organization
Taking Control of Open Source Software in Your Organization For IT Development Executives Looking to Accelerate Developer Use of Open Source Software (OSS) as part of a Multi-source Development Process
More informationSOCIAL MEDIA MINING. Behavior Analytics
SOCIAL MEDIA MINING Behavior Analytics Dear instructors/users of these slides: Please feel free to include these slides in your own material, or modify them as you see fit. If you decide to incorporate
More informationIntroduction to Pattern Recognition
Introduction to Pattern Recognition Dr. Ouiem Bchir 1 / 40 1 Human Perception Humans have developed highly sophisticated skills for sensing their environment and taking actions according to what they observe,
More informationSalford Predictive Modeler. Powerful machine learning software for developing predictive, descriptive, and analytical models.
Powerful machine learning software for developing predictive, descriptive, and analytical models. The Company Minitab helps companies and institutions to spot trends, solve problems and discover valuable
More informationText Mining. Theory and Applications Anurag Nagar
Text Mining Theory and Applications Anurag Nagar Topics Introduction What is Text Mining Features of Text Document Representation Vector Space Model Document Similarities Document Classification and Clustering
More information